Field Mapping of Knowledge Structure

1 Introduction: Mapping of broad research field around AI, industry and innovation dynamics

Here are preliminary results of the bibliometric mapping of the research field. Its purpose is:

• To map the broader research community and distinct research field the department contributes to.
• Identify core knowledge bases, research areas trends and topics.
• Highlight the positioning of the department within this dynamics.

The method for the research-field-mapping can be reviewed here:

Rakas, M., & Hain, D. S. (2019). The state of innovation system research: What happens beneath the surface?. Research Policy, 48(9), 103787.

Note: The analysis in this document depicts the larger research field around the department, thereby all analysis results are based on the publications of the department plus related.

1.1 Seed articles

• The methodology takes a set of self-selected seed articles as point of departure.
• For every of these seed articles, the 2000 articles with the highest bibliographic coupling are extracted. ’ They jointly represent the larger research field.
• The following seedds are used in this analysis

Converting your scopus collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

2 General Overview over the research field

Note: This section provides basic descriptives of th identified research fielld, including number of articles over time, countries, institutions, and authors. See Technical descriptionfor additional explanations.

2.1 Main Indicators: Publications, Authors, Countries

MAIN INFORMATION ABOUT DATA

 Timespan                              1992 : 2023 
 Sources (Journals, Books, etc)        331 
 Documents                             805 
 Annual Growth Rate %                  8.04 
 Document Average Age                  4.31 
 Average citations per doc             99.49 
 Average citations per year per doc    22.23 
 References                            57537 
 
DOCUMENT TYPES                     
              13 
 article      792 
 
DOCUMENT CONTENTS
 Keywords Plus (ID)                    2522 
 Author's Keywords (DE)                2367 
 
AUTHORS
 Authors                               2164 
 Author Appearances                    2680 
 Authors of single-authored docs       93 
 
AUTHORS COLLABORATION
 Single-authored docs                  99 
 Documents per Author                  0.372 
 Co-Authors per Doc                    3.33 
 International co-authorships %        46.09 
 

Annual Scientific Production

Annual Percentage Growth Rate 8.04 


Most Productive Authors

Top manuscripts per citations

Corresponding Author's Countries

SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

Most Relevant Sources

Most Relevant Keywords

NA

2.2 Main institutions, journals, keywords

3 Topic modelling

Note: Here, we report the results of a BERTYopic topic-modelling (basically, clustering on words) on all title+abstract texts. Identified topics can be interpreted as broad themes in the research field. See Technical descriptionfor additional explanations.

3.1 Automated (LMM) summary

Works a bit less precise, since it only has titles and no abstracts

Label: TP 0: Big Data's Strategic Value  
  Description: The provided scientific articles predominantly focus on understanding the strategic value of big data analytics (BDA) and its impact on business performance and innovation. They approach the subject from a foundation of various theories, notably the resource-based view, dynamic capabilities, and information technology business value. The research frameworks utilized often intertwine big data capabilities with organizational readiness, digital transformation, and design thinking. From an industry perspective, manufacturing and consultation seem to be highlighted contexts. The implications of these studies suggest that BDA can drastically transform business decision-making processes, drive competitive advantages, and bring about profound changes in operations and production management. However, the actual value derived from big data is contingent on several factors including organizational design, technological infrastructure, and the human-centric approach to adopting such technologies.  
 
 
Label: TP 1: AI Impact on Sectorial Evolution  
  Description: The provided documents highlight the transformative role of artificial intelligence (AI) across diverse sectors, emphasizing its influence on industry dynamics, innovation, and decision-making. These studies primarily use bibliometric and literature reviews to map the existing knowledge landscape, assessing AI's capabilities, its adoption challenges, and its broader societal implications. Central themes include the AI-human interface in decision-making, particularly in the public sector; AI's potential to reshape the work landscape amidst global events like COVID-19; and the strategic incorporation of AI in various industries, such as oil and gas, legal services, and public relations. Ethical concerns, policy implications, and the necessity for a forward-looking, informed perspective on AI development and deployment are recurrent. These insights collectively underscore AI's potential to revolutionize traditional frameworks, reshaping professional practices while urging caution in its uninhibited adoption.  
 
 
Label: TP 2: AI-Driven Industrial Dynamics  
  Description: The provided literature primarily focuses on the transformative influence of artificial intelligence and associated technologies, like the Internet of Things (IoT), Industry 4.0, and Big Data analytics, on industry dynamics, business models, and innovation. Across diverse sectors, from manufacturing to risk management, AI and digital technologies are reshaping how industries operate, driving process efficiencies, fostering digital transformation, and offering avenues for novel business models. These advancements not only present opportunities for efficiency, sustainability, and quality enhancements but also pose challenges in adoption, scalability, and transformation, necessitating structured implementation approaches. Furthermore, the rapid diffusion and acceptance of these technologies can significantly shape corporate decisions and strategies, necessitating a broader understanding for both researchers and industry professionals. Additionally, the interaction between emerging technologies and global crises, like the COVID-19 pandemic, has underscored the role of AI and digital technologies in responsive and adaptive operations. The collective implications for theory lie in the adaptation of existing models and frameworks, while the practical implications center on strategic transformation and risk management for professionals, and policy considerations for timely and impactful interventions.  
 
 
Label: TP 3: AI in Hospitality Innovation  
  Description: The overarching theme across the documents focuses on the integration, adoption, and implications of artificial intelligence (AI) and robotics in the hospitality sector. These studies predominantly investigate customer perceptions, evaluations, and responses to AI-enabled service encounters, including those with service robots. Key findings highlight that customers often perceive AI and robotic interactions as efficient, and in certain contexts, even preferable to human interactions, particularly in situations that might be deemed embarrassing. Some research points to the importance of anthropomorphism, suggesting that giving robots human-like attributes, such as names, can enhance customer evaluations. Practical implications indicate that the hospitality sector should be proactive in communicating their AI initiatives, ensuring they invest in technology that fosters positive customer experiences. For service providers and policymakers, these insights offer directions for AI deployment that balances efficiency, customer satisfaction, and human touchpoints.  
 
 

4 Knowledge Bases: Co-Citation network analysis

Note: This analysis refers the co-citation analysis, where the cited references and not the original publications are the unit of analysis. Identified knowledge bases can be interpreted as the knowledge foundation the field draws from. See Technical descriptionfor additional explanations.

4.1 Development

4.2 Publications per cluster

name	dgr_int	dgr
Knowledge Base 1: KB 1: Big Data-Driven Business Competence (n = 255, density =14.92)
EREVELLES S. FUKAWA N. SWAYNE L. BIG DATA CONSUMER ANALYTICS AND THE TRANSFORMATION OF MARKETING (2016)	677	1395
AKTER S. WAMBA S.F. GUNASEKARAN A. DUBEY R. CHILDE S.J. HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY AND BUSINESS STRATEGY A...	594	976
GANDOMI A. HAIDER M. BEYOND THE HYPE: BIG DATA CONCEPTS METHODS AND ANALYTICS (2015)	380	504
BARNEY J. FIRM RESOURCES AND SUSTAINED COMPETITIVE ADVANTAGE (1991)	378	822
MCAFEE A. BRYNJOLFSSON E. BIG DATA: THE MANAGEMENT REVOLUTION (2012)	290	392
WAMBA S.F. GUNASEKARAN A. AKTER S. REN S.J.F. DUBEY R. CHILDE S.J. BIG DATA ANALYTICS AND FIRM PERFORMANCE: EFFECTS OF DYNAMIC CAPABILITIES (2017)	283	517
SIVARAJAH U. KAMAL M.M. IRANI Z. WEERAKKODY V. CRITICAL ANALYSIS OF BIG DATA CHALLENGES AND ANALYTICAL METHODS (2017)	217	272
CHEN D.Q. PRESTON D.S. SWINK M. HOW THE USE OF BIG DATA ANALYTICS AFFECTS VALUE CREATION IN SUPPLY CHAIN MANAGEMENT (2015)	212	317
CÔRTE-REAL N. OLIVEIRA T. RUIVO P. ASSESSING BUSINESS VALUE OF BIG DATA ANALYTICS IN EUROPEAN FIRMS (2017)	201	394
GUNASEKARAN A. PAPADOPOULOS T. DUBEY R. WAMBA S.F. CHILDE S.J. HAZEN B. AKTER S. BIG DATA AND PREDICTIVE ANALYTICS FOR SUPPLY CHAIN AND ORGANIZATIO...	198	271
Knowledge Base 2: KB 2: Digital Servitization & Dynamic Capabilities (n = 253, density =9.69)
TEECE D.J. PISANO G. SHUEN A. DYNAMIC CAPABILITIES AND STRATEGIC MANAGEMENT (1997)	619	1464
EISENHARDT K.M. BUILDING THEORIES FROM CASE STUDY RESEARCH (1989)	273	376
TEECE D.J. EXPLICATING DYNAMIC CAPABILITIES: THE NATURE AND MICROFOUNDATIONS OF (SUSTAINABLE)	243	492
EISENHARDT K.M. MARTIN J.A. DYNAMIC CAPABILITIES: WHAT ARE THEY? (2000)	228	666
EISENHARDT K.M. GRAEBNER M.E. THEORY BUILDING FROM CASES: OPPORTUNITIES AND CHALLENGES (2007)	201	223
MATT C. HESS T. BENLIAN A. DIGITAL TRANSFORMATION STRATEGIES (2015)	168	180
NAMBISAN S. WRIGHT M. FELDMAN M. THE DIGITAL TRANSFORMATION OF INNOVATION AND ENTREPRENEURSHIP: PROGRESS CHALLENGES AND KEY THEMES (2019)	165	165
NAMBISAN S. DIGITAL ENTREPRENEURSHIP: TOWARD A DIGITAL TECHNOLOGY PERSPECTIVE OF ENTREPRENEURSHIP (2017)	148	180
TEECE D.J. BUSINESS MODELS BUSINESS STRATEGY AND INNOVATION (2010)	126	132
NAMBISAN S. LYYTINEN K. MAJCHRZAK A. SONG M. DIGITAL INNOVATION MANAGEMENT: REINVENTING INNOVATION MANAGEMENT RESEARCH IN A DIGITAL WORLD (2017)	118	145
Knowledge Base 3: KB 3: Robotics in Hospitality (n = 249, density =17.16)
HUANG M.H. RUST R.T. ARTIFICIAL INTELLIGENCE IN SERVICE (2018)	534	718
WIRTZ J. PATTERSON P.G. KUNZ W.H. GRUBER T. LU V.N. PALUCH S. MARTINS A. BRAVE NEW WORLD: SERVICE ROBOTS IN THE FRONTLINE (2018)	518	633
LU L. CAI R. GURSOY D. DEVELOPING AND VALIDATING A SERVICE ROBOT INTEGRATION WILLINGNESS SCALE (2019)	512	558
MURPHY J. GRETZEL U. PESONEN J. MARKETING ROBOT SERVICES IN HOSPITALITY AND TOURISM: THE ROLE OF ANTHROPOMORPHISM (2019)	449	472
TUNG V.W.S. AU N. EXPLORING CUSTOMER EXPERIENCES WITH ROBOTICS IN HOSPITALITY (2018)	335	349
TUSSYADIAH I.P. PARK S. CONSUMER EVALUATION OF HOTEL SERVICE ROBOTS (2018)	323	333
BELANCHE D. CASALÓ L.V. FLAVIÁN C. SCHEPERS J. SERVICE ROBOT IMPLEMENTATION: A THEORETICAL FRAMEWORK AND RESEARCH AGENDA (2020)	271	289
MENDE M. SCOTT M.L. VAN DOORN J. GREWAL D. SHANKS I. SERVICE ROBOTS RISING: HOW HUMANOID ROBOTS INFLUENCE SERVICE EXPERIENCES AND ELICIT COMPENSATO...	249	265
IVANOV S. GRETZEL U. BEREZINA K. SIGALA M. WEBSTER C. PROGRESS ON ROBOTICS IN HOSPITALITY AND TOURISM: A REVIEW OF THE LITERATURE (2019)	245	261
MURPHY J. HOFACKER C. GRETZEL U. DAWNING OF THE AGE OF ROBOTS IN HOSPITALITY AND TOURISM: CHALLENGES FOR TEACHING AND RESEARCH (2017)	228	242
Knowledge Base 4: KB 4: AI-Driven Organizational Transformation (n = 222, density =10.51)
JARRAHI M.H. ARTIFICIAL INTELLIGENCE AND THE FUTURE OF WORK: HUMAN-AI SYMBIOSIS IN ORGANIZATIONAL DECISION MAKING (2018)	214	253
FREY C.B. OSBORNE M.A. THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION? (2017)	171	224
DAVENPORT T.H. RONANKI R. ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD (2018)	147	171
DUAN Y. EDWARDS J.S. DWIVEDI Y.K. ARTIFICIAL INTELLIGENCE FOR DECISION MAKING IN THE ERA OF BIG DATA–EVOLUTION CHALLENGES AND RESEARCH AGENDA (2019)	132	201
FOUNTAINE T. MCCARTHY B. SALEH T. BUILDING THE AI-POWERED ORGANIZATION (2019)	125	135
BRYNJOLFSSON E. MCAFEE A. (2014)	121	127
GROVER P. KAR A.K. DWIVEDI Y.K. UNDERSTANDING ARTIFICIAL INTELLIGENCE ADOPTION IN OPERATIONS MANAGEMENT: INSIGHTS FROM THE REVIEW OF ACADEMIC LITER...	121	164
DAVENPORT T. GUHA A. GREWAL D. BRESSGOTT T. HOW ARTIFICIAL INTELLIGENCE WILL CHANGE THE FUTURE OF MARKETING (2020)	119	167
WILSON H.J. DAUGHERTY P.R. COLLABORATIVE INTELLIGENCE: HUMANS AND AI ARE JOINING FORCES (2018)	117	134
HAENLEIN M. KAPLAN A. A BRIEF HISTORY OF ARTIFICIAL INTELLIGENCE: ON THE PAST PRESENT AND FUTURE OF ARTIFICIAL INTELLIGENCE (2019)	114	146
Knowledge Base 5: KB 5: Technology Acceptance Dynamics (n = 169, density =11.98)
DAVIS F.D. PERCEIVED USEFULNESS PERCEIVED EASE OF USE AND USER ACCEPTANCE OF INFORMATION TECHNOLOGY (1989)	360	538
FORNELL C. LARCKER D.F. EVALUATING STRUCTURAL EQUATION MODELS WITH UNOBSERVABLE VARIABLES AND MEASUREMENT ERROR (1981)	340	979
VENKATESH V. MORRIS M.G. DAVIS G.B. DAVIS F.D. USER ACCEPTANCE OF INFORMATION TECHNOLOGY: TOWARD A UNIFIED VIEW (2003)	232	370
VENKATESH V. DAVIS F.D. A THEORETICAL EXTENSION OF THE TECHNOLOGY ACCEPTANCE MODEL: FOUR LONGITUDINAL FIELD STUDIES (2000)	178	291
AJZEN I. THE THEORY OF PLANNED BEHAVIOR (1991)	121	136
DAVIS F.D. BAGOZZI R.P. WARSHAW P.R. USER ACCEPTANCE OF COMPUTER TECHNOLOGY: A COMPARISON OF TWO THEORETICAL MODELS (1989)	105	115
PODSAKOFF P.M. MACKENZIE S.B. LEE J.Y. PODSAKOFF N.P. COMMON METHOD BIASES IN BEHAVIORAL RESEARCH: A CRITICAL REVIEW OF THE LITERATURE AND RECOMMEN...	93	255
HENSELER J. RINGLE C.M. SINKOVICS R.R. THE USE OF PARTIAL LEAST SQUARES PATH MODELING IN INTERNATIONAL MARKETING (2009)	85	116
ANDERSON J.C. GERBING D.W. STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH (1988)	83	122
VENKATESH V. THONG J.Y. XU X. CONSUMER ACCEPTANCE AND USE OF INFORMATION TECHNOLOGY: EXTENDING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLO...	78	101
Knowledge Base 6: KB 6: Big Data-Driven Competitive Dynamics (n = 144, density =57.7)
GUPTA M. GEORGE J.F. TOWARD THE DEVELOPMENT OF A BIG DATA ANALYTICS CAPABILITY (2016)	568	1187
LAVALLE S. LESSER E. SHOCKLEY R. HOPKINS M.S. KRUSCHWITZ N. BIG DATA ANALYTICS AND THE PATH FROM INSIGHTS TO VALUE (2011)	443	867
CHEN H. CHIANG R.H. STOREY V.C. BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT (2012)	331	688
GRANT R.M. THE RESOURCE-BASED THEORY OF COMPETITIVE ADVANTAGE: IMPLICATIONS FOR STRATEGY FORMULATION (1991)	297	348
MIKALEF P. PATELI A. INFORMATION TECHNOLOGY-ENABLED DYNAMIC CAPABILITIES AND THEIR INDIRECT EFFECT ON COMPETITIVE PERFORMANCE: FINDINGS FROM PLS-SE...	291	411
VIDGEN R. SHAW S. GRANT D.B. MANAGEMENT CHALLENGES IN CREATING VALUE FROM BUSINESS ANALYTICS (2017)	277	369
ABBASI A. SARKER S. CHIANG R.H. BIG DATA RESEARCH IN INFORMATION SYSTEMS: TOWARD AN INCLUSIVE RESEARCH AGENDA (2016)	276	399
MIKALEF P. PAPPAS I.O. KROGSTIE J. GIANNAKOS M. BIG DATA ANALYTICS CAPABILITIES: A SYSTEMATIC LITERATURE REVIEW AND RESEARCH AGENDA (2018)	275	344
PODSAKOFF P.M. MACKENZIE S.B. LEE J.-Y. PODSAKOFF N.P. COMMON METHOD BIASES IN BEHAVIORAL RESEARCH: A CRITICAL REVIEW OF THE LITERATURE AND RECOMME...	264	447
HAIR J.F. RINGLE C.M. SARSTEDT M. PLS-SEM: INDEED A SILVER BULLET (2011)	250	407

4.3 Automated (LMM) summary

Works a bit less precise, since it only has titles and no abstracts

Label: KB 1: Big Data-Driven Business Competence  
  Description: The provided body of work delves into the transformative influence of Big Data analytics on business and organizational performance. At its core, these articles emphasize the strategic alignment of Big Data capabilities with business objectives, elucidating the potential of data-driven insights in revolutionizing industry dynamics, innovation, and competitive advantage. While the context often extends to supply chain management, marketing, and new product development, a recurring theme revolves around the nexus of dynamic capabilities, organizational agility, and the inherent value proposition of Big Data. Integrating various frameworks, such as the Resource-Based View and Institutional Theory, these studies posit Big Data not just as a technological tool, but as a cornerstone for organizational ambidexterity, adaptive strategy, and sustained competitiveness in a rapidly evolving business landscape.  
 
 
Label: KB 2: Digital Servitization and Dynamic Capabilities  
  Description: The body of work predominantly revolves around the transformative effects of digitalization on industrial dynamics, specifically emphasizing the concepts of "Digital Servitization" and "Dynamic Capabilities." Digital Servitization denotes the use of digital technologies by manufacturing and traditional firms to augment their core offerings with services, leading to value co-creation and new business models. Dynamic Capabilities, on the other hand, focus on the ability of firms to reconfigure and adapt their resources in the face of rapidly changing environments, particularly due to digital disruptions. These capabilities are crucial for firms aiming to sustainably innovate and strategically manage in an increasingly digitized business landscape. Furthermore, a number of these works delve into the methodologies of conducting case studies in such contexts, emphasizing the importance of rigor and triangulation. Overall, the emphasis is on how organizations can strategically leverage digital technologies for enhanced innovation, competitive advantage, and business model transformation.  
 
 
Label: KB 3: Robotics in Hospitality  
  Description: This body of research predominantly delves into the integration and impact of robotic and artificial intelligence (AI) technologies in the hospitality and tourism sectors. These studies often explore customer perceptions and experiences with robotic services, gauging the appropriateness, willingness, and trust towards AI-driven service encounters. The articles investigate the nuanced effects of humanoid robot interactions, including anthropomorphism, on brand experience, rapport building, and consumer responses, especially after service successes or failures. Some works also delve into industry adoption, highlighting the shifting dynamics towards automation, while a few discuss the educational implications and challenges brought about by these technological advancements. The overarching theme reveals the profound transformation of the hospitality industry through AI and robotic interventions, emphasizing both the potential enhancements and challenges they pose to traditional service models.  
 
 
Label: KB 4: AI-Driven Organizational Transformation  
  Description: The given collection of articles predominantly revolves around the transformative impact of artificial intelligence (AI) on organizational dynamics, decision-making, and the broader landscape of work. Key themes that emerge include the symbiotic relationship between humans and AI in decision-making processes, the adaptability and susceptibility of jobs to computerization, and the real-world applications of AI in industries such as marketing, manufacturing, and healthcare. Moreover, these articles frequently touch upon the evolution, challenges, and the resulting organizational opportunities as AI becomes deeply ingrained in the operational and strategic frameworks. Ultimately, the research emphasizes the importance of understanding, adopting, and integrating AI technologies in order to harness their potential benefits while concurrently addressing associated challenges.  
 
 
Label: KB 5: Technology Acceptance Dynamics  
  Description: The overarching theme across these documents delves into the understanding of technology acceptance dynamics, specifically focusing on factors influencing the adoption of emerging and innovative information technologies. Central to the discourse is the Technology Acceptance Model (TAM) and its extensions, emphasizing perceived usefulness and ease of use as primary determinants of tech adoption. The articles also incorporate various theoretical models like the Theory of Planned Behavior and the Unified Theory of Acceptance and Use of Technology, blending them with the context of risks, trust, and privacy concerns. Emphasis is laid on structural equation modeling and related statistical methods to ascertain user behavior and decision-making. Furthermore, the works investigate diverse technologies, ranging from mobile banking to cloud computing, revealing insights into the industry-specific nuances of technology acceptance and innovation dynamics.  
 
 
Label: KB 6: Big Data-Driven Competitive Dynamics  
  Description: The overarching theme of the presented documents revolves around the profound influence of big data analytics on organizational dynamics, competitiveness, and innovation. Central to this research is the idea that big data, when coupled with appropriate analytical capabilities, can provide organizations with a distinct competitive advantage. This competitive advantage is derived not only from the sheer volume of data but from the insights and actionable strategies it can reveal. In parallel, there is a strong emphasis on the methodologies used to validate these insights, like structural equation modeling and PLS-SEM. Finally, the literature also acknowledges the importance of dynamic capabilities, indicating that firms need agility to adapt and extract value from their big data resources, especially in turbulent environments.  
 
 

5 Research Areas: Bibliographic coupling analysis

Note: This analysis refers the bibliographic coupling analysis, where original publications are the unit of analysis. Identified research areas can be interpreted as the field’s current research frontier. See Technical descriptionfor additional explanations.

5.1 Development

5.2 Publications by cluster

AU	PY	TI	dgr_int	TC	TC_year
Research Area 1: RA 1: Big Data Analytics & Dynamic Capabilities (n = 121, density =0.95)
MIKALEF P;PAPPAS IO;KR...	2018	[BIG DATA ANALYTICS CAPABILITIES: A SYSTEMATIC LITERATURE REVIEW AND RESEARCH AGENDA](https://doi.org/10.1007/s10257-017-0362-y)	2.95	391	97.75
MIKALEF P;BOURA M;LEKA...	2019	[BIG DATA ANALYTICS CAPABILITIES AND INNOVATION: THE MEDIATING ROLE OF DYNAMIC CAPABILITIES AND MODERATING EFFECT OF THE EN...](https://doi.org/10.1111/1467-8551.12343)	3.41	292	97.33
MIKALEF P;KROGSTIE J;P...	2020	[EXPLORING THE RELATIONSHIP BETWEEN BIG DATA ANALYTICS CAPABILITY AND COMPETITIVE PERFORMANCE: THE MEDIATING ROLES OF DYNAM...](https://doi.org/10.1016/j.im.2019.05.004)	3.22	309	154.50
RIALTI R;ZOLLO L;FERRA...	2019	[BIG DATA ANALYTICS CAPABILITIES AND PERFORMANCE: EVIDENCE FROM A MODERATED MULTI-MEDIATION MODEL](https://doi.org/10.1016/j.techfore.2019.119781)	2.96	187	62.33
KACHE F;SEURING S	2017	[CHALLENGES AND OPPORTUNITIES OF DIGITAL INFORMATION AT THE INTERSECTION OF BIG DATA ANALYTICS AND SUPPLY CHAIN MANAGEMENT](https://doi.org/10.1108/IJOPM-02-2015-0078)	1.15	455	91.00
DUBEY R;GUNASEKARAN A;...	2020	[BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE PATHWAY TO OPERATIONAL PERFORMANCE UNDER THE EFFECTS OF ENTREPRENEURIAL ORI...](https://doi.org/10.1016/j.ijpe.2019.107599)	1.94	244	122.00
GROVER V;CHIANG RHL;LI...	2018	[CREATING STRATEGIC BUSINESS VALUE FROM BIG DATA ANALYTICS: A RESEARCH FRAMEWORK](https://doi.org/10.1080/07421222.2018.1451951)	0.83	453	113.25
RIALTI R;MARZI G;CIAPP...	2019	[BIG DATA AND DYNAMIC CAPABILITIES: A BIBLIOMETRIC ANALYSIS AND SYSTEMATIC LITERATURE REVIEW](https://doi.org/10.1108/MD-07-2018-0821)	3.19	114	38.00
POPOVIČ A;HACKNEY R;TA...	2018	[THE IMPACT OF BIG DATA ANALYTICS ON FIRMS’ HIGH VALUE BUSINESS PERFORMANCE](https://doi.org/10.1007/s10796-016-9720-4)	1.91	169	42.25
GHASEMAGHAEI M;CALIC G	2020	[ASSESSING THE IMPACT OF BIG DATA ON FIRM INNOVATION PERFORMANCE: BIG DATA IS NOT ALWAYS BETTER DATA](https://doi.org/10.1016/j.jbusres.2019.09.062)	2.17	140	70.00
Research Area 2: RA 2: Robotic Services in Hospitality (n = 115, density =0.73)
KIM SS;KIM J;BADU-BAID...	2021	[PREFERENCE FOR ROBOT SERVICE OR HUMAN SERVICE IN HOTELS? IMPACTS OF THE COVID-19 PANDEMIC](https://doi.org/10.1016/j.ijhm.2020.102795)	2.14	243	243.00
WIRTZ J;PATTERSON PG;K...	2018	[BRAVE NEW WORLD: SERVICE ROBOTS IN THE FRONTLINE](https://doi.org/10.1108/JOSM-04-2018-0119)	0.62	810	202.50
JIANG Y;WEN J	2020	[EFFECTS OF COVID-19 ON HOTEL MARKETING AND MANAGEMENT: A PERSPECTIVE ARTICLE](https://doi.org/10.1108/IJCHM-03-2020-0237)	0.87	454	227.00
DE KERVENOAEL R;HASAN ...	2020	[LEVERAGING HUMAN-ROBOT INTERACTION IN HOSPITALITY SERVICES: INCORPORATING THE ROLE OF PERCEIVED VALUE, EMPATHY, AND INFORM...](https://doi.org/10.1016/j.tourman.2019.104042)	1.79	216	108.00
DAVENPORT T;GUHA A;GRE...	2020	[HOW ARTIFICIAL INTELLIGENCE WILL CHANGE THE FUTURE OF MARKETING](https://doi.org/10.1007/s11747-019-00696-0)	0.62	577	288.50
HUANG M-H;RUST RT	2018	[ARTIFICIAL INTELLIGENCE IN SERVICE](https://doi.org/10.1177/1094670517752459)	0.27	1053	263.25
HUANG M-H;RUST RT	2021	[A STRATEGIC FRAMEWORK FOR ARTIFICIAL INTELLIGENCE IN MARKETING](https://doi.org/10.1007/s11747-020-00749-9)	1.18	233	233.00
CHOI Y;CHOI M;OH M;KIM S	2020	[SERVICE ROBOTS IN HOTELS: UNDERSTANDING THE SERVICE QUALITY PERCEPTIONS OF HUMAN-ROBOT INTERACTION](https://doi.org/10.1080/19368623.2020.1703871)	1.48	165	82.50
GURSOY D;CHI OH;LU L;N...	2019	[CONSUMERS ACCEPTANCE OF ARTIFICIALLY INTELLIGENT (AI) DEVICE USE IN SERVICE DELIVERY](https://doi.org/10.1016/j.ijinfomgt.2019.03.008)	0.70	340	113.33
TUSSYADIAH I	2020	[A REVIEW OF RESEARCH INTO AUTOMATION IN TOURISM: LAUNCHING THE ANNALS OF TOURISM RESEARCH CURATED COLLECTION ON ARTIFICIAL...](https://doi.org/10.1016/j.annals.2020.102883)	0.91	248	124.00
Research Area 3: RA 3: Dynamic Digital Servitization (n = 108, density =0.35)
MATARAZZO M;PENCO L;PR...	2021	[DIGITAL TRANSFORMATION AND CUSTOMER VALUE CREATION IN MADE IN ITALY SMES: A DYNAMIC CAPABILITIES PERSPECTIVE](https://doi.org/10.1016/j.jbusres.2020.10.033)	0.89	279	279.00
RACHINGER M;RAUTER R;M...	2019	[DIGITALIZATION AND ITS INFLUENCE ON BUSINESS MODEL INNOVATION](https://doi.org/10.1108/JMTM-01-2018-0020)	0.49	400	133.33
NADKARNI S;PRÜGL R	2021	[DIGITAL TRANSFORMATION: A REVIEW, SYNTHESIS AND OPPORTUNITIES FOR FUTURE RESEARCH](https://doi.org/10.1007/s11301-020-00185-7)	0.81	188	188.00
BAI C;DALLASEGA P;ORZE...	2020	[INDUSTRY 4.0 TECHNOLOGIES ASSESSMENT: A SUSTAINABILITY PERSPECTIVE](https://doi.org/10.1016/j.ijpe.2020.107776)	0.30	458	229.00
SAARIKKO T;WESTERGREN ...	2020	[DIGITAL TRANSFORMATION: FIVE RECOMMENDATIONS FOR THE DIGITALLY CONSCIOUS FIRM](https://doi.org/10.1016/j.bushor.2020.07.005)	1.01	121	60.50
CULOT G;NASSIMBENI G;O...	2020	[BEHIND THE DEFINITION OF INDUSTRY 4.0: ANALYSIS AND OPEN QUESTIONS](https://doi.org/10.1016/j.ijpe.2020.107617)	0.36	302	151.00
BENITEZ GB;AYALA NF;FR...	2020	[INDUSTRY 4.0 INNOVATION ECOSYSTEMS: AN EVOLUTIONARY PERSPECTIVE ON VALUE COCREATION](https://doi.org/10.1016/j.ijpe.2020.107735)	0.45	221	110.50
GUO H;YANG Z;HUANG R;G...	2020	[THE DIGITALIZATION AND PUBLIC CRISIS RESPONSES OF SMALL AND MEDIUM ENTERPRISES: IMPLICATIONS FROM A COVID-19 SURVEY](https://doi.org/10.1186/s11782-020-00087-1)	0.64	141	70.50
LINDE L;SJÖDIN D;PARID...	2021	[DYNAMIC CAPABILITIES FOR ECOSYSTEM ORCHESTRATION A CAPABILITY-BASED FRAMEWORK FOR SMART CITY INNOVATION INITIATIVES](https://doi.org/10.1016/j.techfore.2021.120614)	0.97	91	91.00
SOLUK J;KAMMERLANDER N	2021	[DIGITAL TRANSFORMATION IN FAMILY-OWNED MITTELSTAND FIRMS: A DYNAMIC CAPABILITIES PERSPECTIVE](https://doi.org/10.1080/0960085X.2020.1857666)	1.01	85	85.00
Research Area 4: RA 4: Digitalization-Driven Labor Dynamics (n = 93, density =0.28)
ACEMOGLU D;RESTREPO P	2020	[ROBOTS AND JOBS: EVIDENCE FROM US LABOR MARKETS](https://doi.org/10.1086/705716)	0.87	582	291.00
ARNTZ M;GREGORY T;ZIER...	2017	[REVISITING THE RISK OF AUTOMATION](https://doi.org/10.1016/j.econlet.2017.07.001)	0.66	204	40.80
HIRSCHI A	2018	[THE FOURTH INDUSTRIAL REVOLUTION: ISSUES AND IMPLICATIONS FOR CAREER RESEARCH AND PRACTICE](https://doi.org/10.1002/cdq.12142)	0.62	207	51.75
RAISCH S;KRAKOWSKI S	2021	[ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION–AUGMENTATION PARADOX](https://doi.org/10.5465/AMR.2018.0072)	0.36	319	319.00
AUTOR D;SALOMONS A	2018	[IS AUTOMATION LABOR SHARE–DISPLACING? PRODUCTIVITY GROWTH, EMPLOYMENT, AND THE LABOR SHARE](https://doi.org/10.1353/eca.2018.0000)	0.80	121	30.25
FARAJ S;PACHIDI S;SAYE...	2018	[WORKING AND ORGANIZING IN THE AGE OF THE LEARNING ALGORITHM](https://doi.org/10.1016/j.infoandorg.2018.02.005)	0.27	245	61.25
SHESTAKOFSKY B	2017	[WORKING ALGORITHMS: SOFTWARE AUTOMATION AND THE FUTURE OF WORK](https://doi.org/10.1177/0730888417726119)	0.77	82	16.40
RUSSELL S;DEWEY D;TEGM...	2015	[RESEARCH PRIORITIES FOR ROBUST AND BENEFICIAL ARTIFICIAL INTELLIGENCE](https://doi.org/10.1609/aimag.v36i4.2577)	0.20	319	45.57
MCCLURE PK	2018	[“YOU’RE FIRED,” SAYS THE ROBOT: THE RISE OF AUTOMATION IN THE WORKPLACE, TECHNOPHOBES, AND FEARS OF UNEMPLOYMENT](https://doi.org/10.1177/0894439317698637)	0.44	132	33.00
BOYD R;HOLTON RJ	2018	[TECHNOLOGY, INNOVATION, EMPLOYMENT AND POWER: DOES ROBOTICS AND ARTIFICIAL INTELLIGENCE REALLY MEAN SOCIAL TRANSFORMATION?](https://doi.org/10.1177/1440783317726591)	0.50	97	24.25
Research Area 5: RA 5: Open Innovation & Sustainable Digitalization (n = 80, density =0.3)
DONTHU N;KUMAR S;MUKHE...	2021	[HOW TO CONDUCT A BIBLIOMETRIC ANALYSIS: AN OVERVIEW AND GUIDELINES](https://doi.org/10.1016/j.jbusres.2021.04.070)	0.62	1648	1648.00
SRINIVASAN R;LILIEN GL...	2002	[TECHNOLOGICAL OPPORTUNISM AND RADICAL TECHNOLOGY ADOPTION: AN APPLICATION TO E-BUSINESS](https://doi.org/10.1509/jmkg.66.3.47.18508)	0.37	423	21.15
DI GANGI PM;WASKO M	2009	[STEAL MY IDEA! ORGANIZATIONAL ADOPTION OF USER INNOVATIONS FROM A USER INNOVATION COMMUNITY: A CASE STUDY OF DELL IDEASTORM](https://doi.org/10.1016/j.dss.2009.04.004)	0.48	297	22.85
OPRESNIK D;TAISCH M	2015	[THE VALUE OF BIG DATA IN SERVITIZATION](https://doi.org/10.1016/j.ijpe.2014.12.036)	0.32	368	52.57
VAN DE VRANDE V;VANHAV...	2010	[BROADENING THE SCOPE OF OPEN INNOVATION: PAST RESEARCH, CURRENT STATE AND FUTURE DIRECTIONS](https://doi.org/10.1504/IJTM.2010.035974)	0.80	135	11.25
BRAGANZA A;BROOKS L;NE...	2017	[RESOURCE MANAGEMENT IN BIG DATA INITIATIVES: PROCESSES AND DYNAMIC CAPABILITIES](https://doi.org/10.1016/j.jbusres.2016.08.006)	0.53	190	38.00
LICHTENTHALER U;ERNST H	2009	[OPENING UP THE INNOVATION PROCESS: THE ROLE OF TECHNOLOGY AGGRESSIVENESS](https://doi.org/10.1111/j.1467-9310.2008.00522.x)	0.57	151	11.62
CHEN M-J;SU K-H;TSAI W	2007	[COMPETITIVE TENSION: THE AWARENESS-MOTIVATION-CAPABILITY PERSPECTIVE](https://doi.org/10.5465/AMJ.2007.24162081)	0.21	411	27.40
KOTABE M;MURRAY JY	2004	[GLOBAL SOURCING STRATEGY AND SUSTAINABLE COMPETITIVE ADVANTAGE](https://doi.org/10.1016/j.indmarman.2003.08.004)	0.34	248	13.78
ZAHRA SA;GEORGE G	2002	[THE NET-ENABLED BUSINESS INNOVATION CYCLE AND THE EVOLUTION OF DYNAMIC CAPABILITIES](https://doi.org/10.1287/isre.13.2.147.90)	0.30	222	11.10
Research Area 6: RA 6: AI-Driven Organizational Transformation (n = 72, density =0.24)
DWIVEDI YK;HUGHES L;IS...	2021	[ARTIFICIAL INTELLIGENCE (AI): MULTIDISCIPLINARY PERSPECTIVES ON EMERGING CHALLENGES, OPPORTUNITIES, AND AGENDA FOR RESEARC...](https://doi.org/10.1016/j.ijinfomgt.2019.08.002)	0.26	730	730.00
DI VAIO A;PALLADINO R;...	2020	[ARTIFICIAL INTELLIGENCE AND BUSINESS MODELS IN THE SUSTAINABLE DEVELOPMENT GOALS PERSPECTIVE: A SYSTEMATIC LITERATURE REVIEW](https://doi.org/10.1016/j.jbusres.2020.08.019)	0.34	255	127.50
MAKARIUS EE;MUKHERJEE ...	2020	[RISING WITH THE MACHINES: A SOCIOTECHNICAL FRAMEWORK FOR BRINGING ARTIFICIAL INTELLIGENCE INTO THE ORGANIZATION](https://doi.org/10.1016/j.jbusres.2020.07.045)	0.59	131	65.50
TOORAJIPOUR R;SOHRABPO...	2021	[ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW](https://doi.org/10.1016/j.jbusres.2020.09.009)	0.19	203	203.00
GROVER P;KAR AK;DWIVED...	2022	[UNDERSTANDING ARTIFICIAL INTELLIGENCE ADOPTION IN OPERATIONS MANAGEMENT: INSIGHTS FROM THE REVIEW OF ACADEMIC LITERATURE A...](https://doi.org/10.1007/s10479-020-03683-9)	0.38	97	Inf
BELHADI A;MANI V;KAMBL...	2021	[ARTIFICIAL INTELLIGENCE-DRIVEN INNOVATION FOR ENHANCING SUPPLY CHAIN RESILIENCE AND PERFORMANCE UNDER THE EFFECT OF SUPPLY...](https://doi.org/10.1007/s10479-021-03956-x)	0.32	106	106.00
PASCHEN U;PITT C;KIETZ...	2020	[ARTIFICIAL INTELLIGENCE: BUILDING BLOCKS AND AN INNOVATION TYPOLOGY](https://doi.org/10.1016/j.bushor.2019.10.004)	0.28	108	54.00
FOSSO WAMBA S;BAWACK R...	2021	[ARE WE PREPARING FOR A GOOD AI SOCIETY? A BIBLIOMETRIC REVIEW AND RESEARCH AGENDA](https://doi.org/10.1016/j.techfore.2020.120482)	0.34	86	86.00
CHALMERS D;MACKENZIE N...	2021	[ARTIFICIAL INTELLIGENCE AND ENTREPRENEURSHIP: IMPLICATIONS FOR VENTURE CREATION IN THE FOURTH INDUSTRIAL REVOLUTION](https://doi.org/10.1177/1042258720934581)	0.32	90	90.00
KEDING C	2021	[UNDERSTANDING THE INTERPLAY OF ARTIFICIAL INTELLIGENCE AND STRATEGIC MANAGEMENT: FOUR DECADES OF RESEARCH IN REVIEW](https://doi.org/10.1007/s11301-020-00181-x)	0.55	52	52.00
Research Area 7: RA 7: AI-Driven Technological Adoption (n = 70, density =0.69)
OLIVEIRA T;FARIA M;THO...	2014	[EXTENDING THE UNDERSTANDING OF MOBILE BANKING ADOPTION: WHEN UTAUT MEETS TTF AND ITM](https://doi.org/10.1016/j.ijinfomgt.2014.06.004)	1.01	514	64.25
OLIVEIRA T;THOMAS M;BA...	2016	[MOBILE PAYMENT: UNDERSTANDING THE DETERMINANTS OF CUSTOMER ADOPTION AND INTENTION TO RECOMMEND THE TECHNOLOGY](https://doi.org/10.1016/j.chb.2016.03.030)	0.63	690	115.00
BAPTISTA G;OLIVEIRA T	2015	[UNDERSTANDING MOBILE BANKING: THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY COMBINED WITH CULTURAL MODERATORS](https://doi.org/10.1016/j.chb.2015.04.024)	0.76	513	73.29
HENGSTLER M;ENKEL E;DU...	2016	[APPLIED ARTIFICIAL INTELLIGENCE AND TRUST-THE CASE OF AUTONOMOUS VEHICLES AND MEDICAL ASSISTANCE DEVICES](https://doi.org/10.1016/j.techfore.2015.12.014)	0.57	346	57.67
SHANKAR A;DATTA B	2018	[FACTORS AFFECTING MOBILE PAYMENT ADOPTION INTENTION: AN INDIAN PERSPECTIVE](https://doi.org/10.1177/0972150918757870)	1.03	155	38.75
MORRIS MG;DILLON A	1997	[HOW USER PERCEPTIONS INFLUENCE SOFTWARE USE](https://doi.org/10.1109/52.595956)	0.76	200	8.00
PUKLAVEC B;OLIVEIRA T;...	2018	[UNDERSTANDING THE DETERMINANTS OF BUSINESS INTELLIGENCE SYSTEM ADOPTION STAGES AN EMPIRICAL STUDY OF SMES](https://doi.org/10.1108/IMDS-05-2017-0170)	1.01	107	26.75
TALUKDER MS;CHIONG R;B...	2019	[ACCEPTANCE AND USE PREDICTORS OF FITNESS WEARABLE TECHNOLOGY AND INTENTION TO RECOMMEND: AN EMPIRICAL STUDY](https://doi.org/10.1108/IMDS-01-2018-0009)	0.83	127	42.33
CHATTERJEE S;RANA NP;D...	2021	[UNDERSTANDING AI ADOPTION IN MANUFACTURING AND PRODUCTION FIRMS USING AN INTEGRATED TAM-TOE MODEL](https://doi.org/10.1016/j.techfore.2021.120880)	0.76	125	125.00
SIVATHANU B	2019	[ADOPTION OF DIGITAL PAYMENT SYSTEMS IN THE ERA OF DEMONETIZATION IN INDIA: AN EMPIRICAL STUDY](https://doi.org/10.1108/JSTPM-07-2017-0033)	0.66	142	47.33

5.3 Automated (LMM) summary

Label: RA 1: Big Data Analytics and Dynamic Capabilities  
  Description: The prevailing research theme among the provided articles is the exploration of big data analytics capabilities (BDAC) and their influence on firm performance, specifically through the lens of the resource-based view and dynamic capabilities theory. These articles collectively argue that big data analytics are not just tools for extracting insights, but strategic assets that can significantly bolster a firm's dynamic capabilities, driving both incremental and radical innovation. By leveraging big data analytics, firms can enhance their marketing, technological, and operational capabilities, leading to sustainable competitive advantages. The environment, such as market dynamism and heterogeneity, can influence the effect of these capabilities. For policymakers and professionals, these findings highlight the strategic importance of investing in BDAC, and the necessary organizational structures and processes to effectively harness them.  
 
 
Label: RA 2: Robotic Services in Hospitality  
  Description: Research on robotic and AI services in the hospitality industry has seen a significant surge, especially in the context of their interaction with humans. Studies have explored hotel managers' perceptions, consumers' preferences, and the impact of global events such as the COVID-19 pandemic on the acceptance of robotic services. A recurrent theme is the balancing act between human touch, emotional intelligence, and the efficiency of robots. There's a clear indication that while robots might be favored for repetitive or potentially hazardous tasks, humans still hold a unique place for tasks requiring sincerity, empathy, and emotional understanding. Theoretical implications highlight the evolving dynamics of human-robot interaction in service environments, and the practical implications suggest a cautious yet forward-moving approach to integrating robots, ensuring that customer value and trust are not compromised. The overarching narrative supports the twin transition of digitalization and sustainability in the hospitality sector, pointing toward a future where robots and humans collaboratively enhance service quality and operational efficiency.  
 
 
Label: RA 3: Dynamic Digital Servitization  
  Description: The overarching theme across these documents is the integration and transformation of businesses through digitalization, with a specific emphasis on the concept of servitization. Drawing on the theory of dynamic capabilities, the articles collectively explore how firms sense, seize, and reconfigure their strategies and operations in response to digital advancements. This digital shift often revolves around the evolution of traditional business models towards ones that incorporate smart, interconnected technologies, such as AI and IoT. The potential implications of these studies are profound. They inform theory by offering nuanced insights into the interaction of business models, digital technologies, and dynamic capabilities. From a practical standpoint, managers gain actionable guidance on navigating digital transformation effectively. Lastly, policymakers can derive insights about the changing nature of industries and the competencies needed for future success.  
 
 
Label: RA 4: Digitalization-Driven Labor Dynamics  
  Description: The confluence of digitalization and sustainability is significantly reshaping labor market dynamics. Central to this transformation are technologies like industrial robots, automation, and software algorithms that potentially substitute or complement human labor. While these advancements can lead to job displacement, particularly in routine-intensive roles, they also offer transformative opportunities, engendering new forms of human-software collaboration. Notably, the risk of automation is often overestimated, overlooking task heterogeneity within occupations and the adaptability of jobs in the digital age. This overarching theme sheds light on the imperative for academia, policymakers, and professionals to adapt career development frameworks, advocate for displaced workers, and reimagine education to align with a progressively digitalized labor landscape.  
 
 
Label: RA 5: Open Innovation and Sustainable Digitalization  
  Description: The overarching research theme in the provided documents centers on the intersection of open innovation and sustainable digitalization in various organizational contexts. At its core, these studies emphasize the evolution and broadening of the open innovation paradigm, showing its expansion from individual actors to larger ecosystems, such as national innovation systems. Crucially, there's an inherent call to integrate open innovation with other management disciplines and relate contemporary insights to established theories. Bibliometric and social network analyses serve as common methodological approaches to trace the theoretical evolution of this field. Furthermore, the research delves into the specifics of implementing open innovation strategies, understanding relational capabilities in service sectors like hospitality, and leveraging big data for enhanced resource management. These insights not only enrich academic theory but also provide valuable guidelines for policymakers and industry professionals navigating the twin transition towards heightened digitalization and sustainability.  
 
 
Label: RA 6: AI-Driven Organizational Transformation  
  Description: The burgeoning field of artificial intelligence (AI) is revolutionizing various sectors, notably reshaping the dynamics of organizational practices and industry innovations. Central to these studies is the socio-technical systems (STS) theory, emphasizing the synergy between AI and human collaboration, which is pivotal for achieving desired business outcomes. A recurrent theme, the integration of AI in organizational frameworks is observed to influence areas like strategic management, human resource management, operations, and entrepreneurial ventures. While AI presents promising advantages, it's imperative to address challenges such as the social and economic implications of AI, particularly its influence on traditional entities. Furthermore, in light of global disruptions and a push towards sustainability, AI's potential in bolstering supply chain resilience and promoting sustainable business models anchored in the UN's 2030 agenda is gaining prominence. These collective insights are paramount not just for the academic community but also for industry practitioners and policymakers, urging them to harness AI's capabilities optimally, fostering innovation and long-term growth.  
 
 
Label: RA 7: AI-Driven Technological Adoption  
  Description: Research themes consistently focus on understanding the determinants and influencing factors behind the adoption of new technologies, especially those enhanced by artificial intelligence. They utilize frameworks like the Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) to examine individual and organizational perceptions and intentions towards the use of these technologies. Factors such as perceived usefulness, perceived ease of use, compatibility, trust, and risk perceptions emerge as significant in shaping adoption decisions. The implications of these findings are vast; for theory, they provide an enhanced understanding of tech adoption in the era of AI; for policy, they highlight the need to address trust and risk issues; and for professionals, they guide strategies for the successful implementation and recommendation of new technologies. The context of application spans industries from business intelligence to mobile banking, e-governance, tourism, and health wearables, highlighting the ubiquity and relevance of AI-infused technologies in contemporary settings.  
 
 

5.4 Further Analysis

6 Overal interplay

6.1 Knowledge Bases, Research Areas & Topics Interaction

6.2 Joint Overview over Knowledge Bases, Research Areas, and Topics

This plot shows the connection of publications in the research areas to knowledge bases (by citations) and topics (by gamma, document-topic weight)

7 Collaboration

7.1 Collaboration network

8 Endnotes